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Predicting floods with Flickr tags

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Tkachenko, Nataliya, Jarvis, Stephen A. and Procter, Rob (2017) Predicting floods with Flickr tags. PLoS One, 12 (2). e0172870. doi:10.1371/journal.pone.0172870 ISSN 1932-6203.

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Official URL: http://dx.doi.org/10.1371/journal.pone.0172870

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Abstract

Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags ‘#Sandy’ or ‘#flooding’), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.

Item Type: Journal Article
Subjects: G Geography. Anthropology. Recreation > GB Physical geography
H Social Sciences > HM Sociology
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Natural disaster warning systems, Floods, Online social networks, Metadata, User-generated content
Journal or Publication Title: PLoS One
Publisher: Public Library of Science
ISSN: 1932-6203
Official Date: 24 February 2017
Dates:
DateEvent
24 February 2017Published
11 February 2017Accepted
11 September 2016Submitted
Volume: 12
Number: 2
Article Number: e0172870
DOI: 10.1371/journal.pone.0172870
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 27 February 2017
Date of first compliant Open Access: 27 February 2017
Funder: Engineering and Physical Sciences Research Council (EPSRC)
Grant number: EP/L016400/1 (EPSRC)

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